For real-time CMR under exercise stress, nnU-Net achieves Dice’s coefficients of 0.92 for LV, 0.Eighty five for MYO, and 0.83 for RV and the mean absolute differences between nnU-Net and reference are 11.Four mL for EDV, 2.9 mL for ESV and 3.6 % for EF. For nnU-Net, the absolute and relative differences of EDV, ESV, and EF are lower than the inter-observer variability, though the direct comparability is restricted because pictures had been newly chosen for intra- and inter-observer variability, whereas the nnU-Net segmentation has been in comparison with the manually corrected contours of the same photos. For actual-time CMR at relaxation, nnU-Net achieves Dice’s coefficients of 0.Ninety four for LV, 0.89 for MYO, and 0.Ninety for RV and the mean absolute differences between nnU-Net and the reference are 2.9 mL for EDV, 3.5 mL for ESV and 2.6 % for EF. Consequently, the 2D model of nnU-Net with weights pre-educated on the ACDC dataset was applied on single photos of the dataset for all cine and actual-time measurements.

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